IGE: STEM Graduate training in Data Science: solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training

IGE：数据科学领域的 STEM 研究生培训：以解决方案为导向、学生主导、基于团队、计算丰富的 (SOLSTICE) 培训

• 批准号: 1855886
• 负责人: Elena Naumova
• 金额: $ 49.96万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855886&HistoricalAwards=false
• 关键词: IGE; STEM; Graduate; training; Data

There has been a rapid increase in both the amount and type of data collected in many fields of science, technology and engineering; this requires new approaches to train graduate students to meet these needs. The new data revolution calls for a new data-savvy workforce capable of using data effectively to answer important scientific and practical questions and to communicate the results to a broad audience. These data scientists should have strong technical and leadership skills and the ability to work in teams comprising different and complementary expertise. To meet this training need, the National Science Foundation Innovations of Graduate Education (IGE) award to Tufts University will test an innovative approach to project-based learning, teaching graduate students in data-intensive fields using real datasets to solve real-world problems. This solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training environment will offer multidisciplinary team role-play and experience in data analytics and scientific communication. This project will evaluate the effectiveness of the SOLSTICE approach in providing an adaptable method for instructors in a range of fields that use large datasets at diverse institution types. As the SOLSTICE approach is refined, it will pave the way for students to enter the workforce with strong technical skills in data analytics and be effective members of research teams. The project will answer two overarching research questions: (1) To what extent does the SOLSTICE approach provide opportunities for students to achieve 21st-century data intensive knowledge, skills, and attitudes? and (2) Are there variations in students' achievement over time, across different courses that use the SOLSTICE approach, and across different STEM disciplines? The approach consists of three components: (1) 3D Role Play, in which each student plays the role of Team Lead, Collaborator, and Reviewer in three different interdisciplinary teams aiming to develop, execute and communicate a research project based on authentic secondary data analyses; (2) Feedback on Feedback, which provides students with experience in giving and receiving effective critiques to their peers to strengthen inter-team communication; and (3) Data Analysis Roadmap, which offers knowledge-building guides and examples of key elements for efficient data analysis plans and other documents needed for effective communication of results. The approach will be tested and evaluated in six data-intensive courses in the Friedman School of Nutrition and in the School of Engineering, with approximately 270 participants monitored before, during, and for at least six months after participation in a SOLSTICE-based course. Educational resources will be developed based on successful components to guide replications of the training process, to train faculty in the SOLSTICE approach, and to disseminate it to other STEM fields. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多科学，技术和工程领域收集的数据的数量和类型都很快增加了。这需要新的方法来培训研究生以满足这些需求。 新的数据革命要求一个新的精通数据的劳动力，能够有效地使用数据来回答重要的科学和实用问题，并将结果传达给广泛的受众。这些数据科学家应具有强大的技术和领导能力，并具有在包括不同和补充专业知识的团队中工作的能力。为了满足这一培训需求，塔夫茨大学的国家科学基金会研究生教育奖（IGE）奖将对基于项目的学习进行创新方法，使用真实数据集在数据密集型领域中教授研究生来解决现实世界中的问题。这个面向解决方案的，以学生为主导的，基于团队的，富含计算的培训环境将在数据分析和科学交流中提供多学科团队的角色扮演和经验。该项目将评估冬至方法在为多种机构类型中使用大型数据集的一系列领域的讲师提供适应性方法的有效性。随着冬至方法的完善，它将为学生在数据分析方面具有强大技术技能的劳动力铺平道路，并成为研究团队的有效成员。该项目将回答两个总体研究问题：（1）求解方法在多大程度上为学生提供了实现21世纪数据密集知识，技能和态度的机会？ （2）学生的成就随着时间的流逝，跨越冬至的不同课程以及跨不同的STEM学科的不同课程是否存在差异？该方法由三个组成部分组成：（1）3D角色扮演，其中每个学生在三个不同的跨学科团队中扮演团队负责人，合作者和审稿人的角色，旨在开发，执行和传达基于真实的二级数据分析的研究项目； （2）反馈的反馈，该反馈为学生提供了对同行的有效批评的经验，以加强团队间的沟通； （3）数据分析路线图，该路线图提供了知识构建指南和关键要素的示例，以进行有效的数据分析计划以及有效沟通结果所需的其他文档。该方法将在弗里德曼营养学院和工程学院的六个数据密集型课程中进行测试和评估，并在参加基于冬至的课程后至少六个月对约有270名参与者进行了监视。将根据成功的组成部分开发教育资源，以指导培训过程的复制，以冬至方法培训教师并将其传播到其他STEM领域。研究生教育（IGE）计划的创新侧重于研究生教育研究。 IGE的目标是试点，测试和验证创新方法来研究生教育，并产生将这些方法转移到更广泛社区所需的知识。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。